Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

Over the past few years, machine learning has emerged as a powerful computational tool to
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …

Many important challenges in science and technology can be cast as optimization problems.
When viewed in a statistical physics framework, these can be tackled by simulated …

We introduce interpretable siamese neural networks (SNNs) for similarity detection to the
field of theoretical physics. More precisely, we apply SNNs to events in special relativity, the …

Machine learning is becoming widely used in condensed matter physics. Inspired by the
concept of image super-resolution, we propose a method to increase the size of lattice spin …

We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of
machine learning methods. In our approach, we treat the output from a convolutional neural …

Monte Carlo (MC) simulations are essential computational approaches with widespread use
throughout all areas of science. We present a method for accelerating lattice MC simulations …

Microscopically understanding and classifying phases of matter is at the heart of strongly-
correlated quantum physics. With quantum simulations, genuine projective measurements …

We introduce a variational wave function for many-body ground states that involves
imaginary-time evolution with two different Hamiltonians in an alternating fashion with …

Artificial neural networks have been widely adopted as ansatzes to study classical and
quantum systems. However, for some notably hard systems, such as those exhibiting …